mlsanityData to 14 Oct 2026

Foundations of Large Language Models

arXiv 2501.09223 ↗First posted 16 Jan 2025cs.CL cs.AI cs.LGcited by 14

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Abstract (arXiv)

This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into six main chapters, each exploring a key area: pre-training, generative models, prompting, alignment, inference, and reasoning. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jan 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 2.

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  1. 2025GRAM: A Generative Foundation Reward Model for Reward Generalizationcited by 25
  2. 2025Large Language Models for Computer-Aided Design: A Surveycited by 18
  3. 2025Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translationcited by 8
  4. 2025Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agentscited by 5
  5. 2026MSRL: Scaling Generative Multimodal Reward Modeling via Multi-Stage Reinforcement Learningcited by 5
  6. 2025Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMscited by 4
  7. 2025SquarechiPO: Differentially Private and Robust chi2-Preference Optimization in Offline Direct Alignmentcited by 3
  8. 2025Probing Preference Representations: A Multi-Dimensional Evaluation and Analysis Method for Reward Modelscited by 3
  9. 2025Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMscited by 2
  10. 2025SasAgent: Multi-Agent AI System for Small-Angle Scattering Data Analysiscited by 2
  11. 2025Generating Privacy Stories From Software Documentationcited by 0
  12. 2025LSM-MS2: A Foundation Model Bridging Spectral Identification and Biological Interpretationcited by 0
2 more
  1. 2025LLMs Can Assist with Proposal Selection at Large User Facilitiescited by 0
  2. 2026LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentationcited by 0